More hub models (#851)

* add all available models

* simplify vision hubconf

hubconf.py

-'''
-This is an example hubconf.py for pytorch/vision repo
-
-## Users can get this published model by calling:
-hub_model = hub.load(
-    'pytorch/vision:master', # repo_owner/repo_name:branch
-    'resnet18', # entrypoint
-    1234, # args for callable [not applicable to resnet]
-    pretrained=True) # kwargs for callable
-
-## Protocol on repo owner side
-1. The "published" models should be at least in a branch/tag. It can't be a random commit.
-2. Repo owner should have the following field defined in hubconf.py
-  2.1 Function/entrypoint with function signature "def resnet18(pretrained=False, *args, **kwargs):"
-  2.2 Pretrained allows users to load pretrained weights from repo owner.
-  2.3 Args and kwargs are passed to the callable _resnet18,
-  2.4 Docstring of function works as a help message, explaining what does the model do and what's
-      the allowed arguments.
-  2.5 Dependencies is a list optionally provided by repo owner, to specify what packages are required
-      to run the model.
-
-## Hub_dir
-
-hub_dir specifies where the intermediate files/folders will be saved. By default this is ~/.torch/hub.
-Users can change it by either setting the environment variable TORCH_HUB_DIR or calling hub.set_dir(PATH_TO_HUB_DIR).
-By default, we don't cleanup files after loading so that users can use cache next time.
-
-## Cache logic
-
-We used the cache by default if it exists in hub_dir.
-Users can force a fresh reload by calling hub.load(..., force_reload=True).
-'''
-
-import torch.utils.model_zoo as model_zoo
-
 # Optional list of dependencies required by the package
-dependencies = ['torch', 'math']
-
-
-def resnet18(pretrained=False, *args, **kwargs):
-    """
-    Resnet18 model
-    pretrained (bool): a recommended kwargs for all entrypoints
-    args & kwargs are arguments for the function
-    """
-    from torchvision.models.resnet import resnet18 as _resnet18
-    model = _resnet18(*args, **kwargs)
-    checkpoint = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(checkpoint, progress=False))
-    return model
-
-
-def resnet50(pretrained=False, *args, **kwargs):
-    """
-    Resnet50 model
-    pretrained (bool): a recommended kwargs for all entrypoints
-    args & kwargs are arguments for the function
-    """
-    from torchvision.models.resnet import resnet50 as _resnet50
-    model = _resnet50(*args, **kwargs)
-    checkpoint = 'https://download.pytorch.org/models/resnet50-19c8e357.pth'
-    if pretrained:
-        model.load_state_dict(model_zoo.load_url(checkpoint, progress=False))
-    return model
+dependencies = ['torch']
+
+from torchvision.models.alexnet import alexnet
+from torchvision.models.densenet import densenet121, densenet169, densenet201, densenet161
+from torchvision.models.inception import inception_v3
+from torchvision.models.resnet import resnet18, resnet34, resnet50, resnet101, resnet152
+from torchvision.models.squeezenet import squeezenet1_0, squeezenet1_1
+from torchvision.models.vgg import vgg11, vgg13, vgg16, vgg19, vgg11_bn, vgg13_bn, vgg16_bn, vgg19_bn